{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelBinarizer,OneHotEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 设置jupyter数据框最大显示的列数\n",
    "pd.set_option('max_columns',100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('../train_data.csv',encoding='gb2312',index_col='id')\n",
    "# test_data = pd.read_csv('../test_data.csv',encoding='gb2312',index_col='id')\n",
    "targets = pd.read_csv('../targets.csv',encoding='gb2312',index_col='id')\n",
    "train = pd.read_csv('../d_train_20180102.csv',encoding='gb2312',index_col='id')\n",
    "test = pd.read_csv('../d_test_A_20180102.csv',encoding='gb2312',index_col='id')\n",
    "train,test = train_data.loc[train.index],train_data.loc[test.index]\n",
    "targets = targets.loc[train.index]\n",
    "# feature_importance = pd.read_csv('../feature_importance.csv',encoding='gb2312')\n",
    "test_real_y = pd.read_csv('../d_answer_a_20180128.csv',encoding='gb2312',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄</th>\n",
       "      <th>体检日期</th>\n",
       "      <th>*天门冬氨酸氨基转换酶</th>\n",
       "      <th>*丙氨酸氨基转换酶</th>\n",
       "      <th>*碱性磷酸酶</th>\n",
       "      <th>*r-谷氨酰基转换酶</th>\n",
       "      <th>*总蛋白</th>\n",
       "      <th>白蛋白</th>\n",
       "      <th>*球蛋白</th>\n",
       "      <th>白球比例</th>\n",
       "      <th>甘油三酯</th>\n",
       "      <th>总胆固醇</th>\n",
       "      <th>高密度脂蛋白胆固醇</th>\n",
       "      <th>低密度脂蛋白胆固醇</th>\n",
       "      <th>尿素</th>\n",
       "      <th>肌酐</th>\n",
       "      <th>尿酸</th>\n",
       "      <th>乙肝表面抗原</th>\n",
       "      <th>乙肝表面抗体</th>\n",
       "      <th>乙肝e抗原</th>\n",
       "      <th>乙肝e抗体</th>\n",
       "      <th>乙肝核心抗体</th>\n",
       "      <th>白细胞计数</th>\n",
       "      <th>红细胞计数</th>\n",
       "      <th>血红蛋白</th>\n",
       "      <th>红细胞压积</th>\n",
       "      <th>红细胞平均体积</th>\n",
       "      <th>红细胞平均血红蛋白量</th>\n",
       "      <th>红细胞平均血红蛋白浓度</th>\n",
       "      <th>红细胞体积分布宽度</th>\n",
       "      <th>血小板计数</th>\n",
       "      <th>血小板平均体积</th>\n",
       "      <th>血小板体积分布宽度</th>\n",
       "      <th>血小板比积</th>\n",
       "      <th>中性粒细胞%</th>\n",
       "      <th>淋巴细胞%</th>\n",
       "      <th>单核细胞%</th>\n",
       "      <th>嗜酸细胞%</th>\n",
       "      <th>嗜碱细胞%</th>\n",
       "      <th>有无蛋白指标</th>\n",
       "      <th>有无固醇指标</th>\n",
       "      <th>有无血指标</th>\n",
       "      <th>有无细胞指标</th>\n",
       "      <th>有无酶指标</th>\n",
       "      <th>有无尿指标</th>\n",
       "      <th>有无肝指标</th>\n",
       "      <th>缺失值数量</th>\n",
       "      <th>酶_mean</th>\n",
       "      <th>酶_max</th>\n",
       "      <th>...</th>\n",
       "      <th>红细胞体积分布宽度*中性粒细胞%</th>\n",
       "      <th>红细胞体积分布宽度*淋巴细胞%</th>\n",
       "      <th>红细胞体积分布宽度*单核细胞%</th>\n",
       "      <th>红细胞体积分布宽度*嗜酸细胞%</th>\n",
       "      <th>红细胞体积分布宽度*嗜碱细胞%</th>\n",
       "      <th>血小板计数*血小板计数</th>\n",
       "      <th>血小板计数*血小板平均体积</th>\n",
       "      <th>血小板计数*血小板体积分布宽度</th>\n",
       "      <th>血小板计数*血小板比积</th>\n",
       "      <th>血小板计数*中性粒细胞%</th>\n",
       "      <th>血小板计数*淋巴细胞%</th>\n",
       "      <th>血小板计数*单核细胞%</th>\n",
       "      <th>血小板计数*嗜酸细胞%</th>\n",
       "      <th>血小板计数*嗜碱细胞%</th>\n",
       "      <th>血小板平均体积*血小板平均体积</th>\n",
       "      <th>血小板平均体积*血小板体积分布宽度</th>\n",
       "      <th>血小板平均体积*血小板比积</th>\n",
       "      <th>血小板平均体积*中性粒细胞%</th>\n",
       "      <th>血小板平均体积*淋巴细胞%</th>\n",
       "      <th>血小板平均体积*单核细胞%</th>\n",
       "      <th>血小板平均体积*嗜酸细胞%</th>\n",
       "      <th>血小板平均体积*嗜碱细胞%</th>\n",
       "      <th>血小板体积分布宽度*血小板体积分布宽度</th>\n",
       "      <th>血小板体积分布宽度*血小板比积</th>\n",
       "      <th>血小板体积分布宽度*中性粒细胞%</th>\n",
       "      <th>血小板体积分布宽度*淋巴细胞%</th>\n",
       "      <th>血小板体积分布宽度*单核细胞%</th>\n",
       "      <th>血小板体积分布宽度*嗜酸细胞%</th>\n",
       "      <th>血小板体积分布宽度*嗜碱细胞%</th>\n",
       "      <th>血小板比积*血小板比积</th>\n",
       "      <th>血小板比积*中性粒细胞%</th>\n",
       "      <th>血小板比积*淋巴细胞%</th>\n",
       "      <th>血小板比积*单核细胞%</th>\n",
       "      <th>血小板比积*嗜酸细胞%</th>\n",
       "      <th>血小板比积*嗜碱细胞%</th>\n",
       "      <th>中性粒细胞%*中性粒细胞%</th>\n",
       "      <th>中性粒细胞%*淋巴细胞%</th>\n",
       "      <th>中性粒细胞%*单核细胞%</th>\n",
       "      <th>中性粒细胞%*嗜酸细胞%</th>\n",
       "      <th>中性粒细胞%*嗜碱细胞%</th>\n",
       "      <th>淋巴细胞%*淋巴细胞%</th>\n",
       "      <th>淋巴细胞%*单核细胞%</th>\n",
       "      <th>淋巴细胞%*嗜酸细胞%</th>\n",
       "      <th>淋巴细胞%*嗜碱细胞%</th>\n",
       "      <th>单核细胞%*单核细胞%</th>\n",
       "      <th>单核细胞%*嗜酸细胞%</th>\n",
       "      <th>单核细胞%*嗜碱细胞%</th>\n",
       "      <th>嗜酸细胞%*嗜酸细胞%</th>\n",
       "      <th>嗜酸细胞%*嗜碱细胞%</th>\n",
       "      <th>嗜碱细胞%*嗜碱细胞%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>24.96</td>\n",
       "      <td>23.10</td>\n",
       "      <td>99.59</td>\n",
       "      <td>20.23</td>\n",
       "      <td>76.88</td>\n",
       "      <td>49.60</td>\n",
       "      <td>27.28</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.31</td>\n",
       "      <td>4.43</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.65</td>\n",
       "      <td>5.87</td>\n",
       "      <td>77.25</td>\n",
       "      <td>349.39</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.21</td>\n",
       "      <td>166.1</td>\n",
       "      <td>0.479</td>\n",
       "      <td>91.9</td>\n",
       "      <td>31.9</td>\n",
       "      <td>347.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>166.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.164</td>\n",
       "      <td>54.1</td>\n",
       "      <td>34.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>41.9700</td>\n",
       "      <td>99.59</td>\n",
       "      <td>...</td>\n",
       "      <td>692.48</td>\n",
       "      <td>437.76</td>\n",
       "      <td>83.20</td>\n",
       "      <td>60.16</td>\n",
       "      <td>7.68</td>\n",
       "      <td>27556.0</td>\n",
       "      <td>1643.4</td>\n",
       "      <td>2888.4</td>\n",
       "      <td>27.224</td>\n",
       "      <td>8980.6</td>\n",
       "      <td>5677.2</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>780.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>98.01</td>\n",
       "      <td>172.26</td>\n",
       "      <td>1.6236</td>\n",
       "      <td>535.59</td>\n",
       "      <td>338.58</td>\n",
       "      <td>64.35</td>\n",
       "      <td>46.53</td>\n",
       "      <td>5.94</td>\n",
       "      <td>302.76</td>\n",
       "      <td>2.8536</td>\n",
       "      <td>941.34</td>\n",
       "      <td>595.08</td>\n",
       "      <td>113.10</td>\n",
       "      <td>81.78</td>\n",
       "      <td>10.44</td>\n",
       "      <td>0.026896</td>\n",
       "      <td>8.8724</td>\n",
       "      <td>5.6088</td>\n",
       "      <td>1.0660</td>\n",
       "      <td>0.7708</td>\n",
       "      <td>0.0984</td>\n",
       "      <td>2926.81</td>\n",
       "      <td>1850.22</td>\n",
       "      <td>351.65</td>\n",
       "      <td>254.27</td>\n",
       "      <td>32.46</td>\n",
       "      <td>1169.64</td>\n",
       "      <td>222.30</td>\n",
       "      <td>160.74</td>\n",
       "      <td>20.52</td>\n",
       "      <td>42.25</td>\n",
       "      <td>30.55</td>\n",
       "      <td>3.90</td>\n",
       "      <td>22.09</td>\n",
       "      <td>2.82</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>24.57</td>\n",
       "      <td>36.25</td>\n",
       "      <td>67.21</td>\n",
       "      <td>79.00</td>\n",
       "      <td>79.43</td>\n",
       "      <td>47.76</td>\n",
       "      <td>31.67</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.81</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2.63</td>\n",
       "      <td>5.26</td>\n",
       "      <td>87.12</td>\n",
       "      <td>486.78</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>7.65</td>\n",
       "      <td>5.21</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.456</td>\n",
       "      <td>87.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>342.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>277.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.260</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>51.7575</td>\n",
       "      <td>79.00</td>\n",
       "      <td>...</td>\n",
       "      <td>696.80</td>\n",
       "      <td>491.78</td>\n",
       "      <td>77.72</td>\n",
       "      <td>62.98</td>\n",
       "      <td>10.72</td>\n",
       "      <td>76729.0</td>\n",
       "      <td>2548.4</td>\n",
       "      <td>2853.1</td>\n",
       "      <td>72.020</td>\n",
       "      <td>14404.0</td>\n",
       "      <td>10165.9</td>\n",
       "      <td>1606.6</td>\n",
       "      <td>1301.9</td>\n",
       "      <td>221.6</td>\n",
       "      <td>84.64</td>\n",
       "      <td>94.76</td>\n",
       "      <td>2.3920</td>\n",
       "      <td>478.40</td>\n",
       "      <td>337.64</td>\n",
       "      <td>53.36</td>\n",
       "      <td>43.24</td>\n",
       "      <td>7.36</td>\n",
       "      <td>106.09</td>\n",
       "      <td>2.6780</td>\n",
       "      <td>535.60</td>\n",
       "      <td>378.01</td>\n",
       "      <td>59.74</td>\n",
       "      <td>48.41</td>\n",
       "      <td>8.24</td>\n",
       "      <td>0.067600</td>\n",
       "      <td>13.5200</td>\n",
       "      <td>9.5420</td>\n",
       "      <td>1.5080</td>\n",
       "      <td>1.2220</td>\n",
       "      <td>0.2080</td>\n",
       "      <td>2704.00</td>\n",
       "      <td>1908.40</td>\n",
       "      <td>301.60</td>\n",
       "      <td>244.40</td>\n",
       "      <td>41.60</td>\n",
       "      <td>1346.89</td>\n",
       "      <td>212.86</td>\n",
       "      <td>172.49</td>\n",
       "      <td>29.36</td>\n",
       "      <td>33.64</td>\n",
       "      <td>27.26</td>\n",
       "      <td>4.64</td>\n",
       "      <td>22.09</td>\n",
       "      <td>3.76</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>26/10/2017</td>\n",
       "      <td>20.82</td>\n",
       "      <td>15.23</td>\n",
       "      <td>63.69</td>\n",
       "      <td>38.17</td>\n",
       "      <td>86.23</td>\n",
       "      <td>48.00</td>\n",
       "      <td>38.23</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.13</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.01</td>\n",
       "      <td>4.77</td>\n",
       "      <td>78.19</td>\n",
       "      <td>452.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.07</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.76</td>\n",
       "      <td>148.8</td>\n",
       "      <td>0.438</td>\n",
       "      <td>91.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>340.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>16.6</td>\n",
       "      <td>0.199</td>\n",
       "      <td>48.1</td>\n",
       "      <td>40.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.4775</td>\n",
       "      <td>63.69</td>\n",
       "      <td>...</td>\n",
       "      <td>625.30</td>\n",
       "      <td>523.90</td>\n",
       "      <td>100.10</td>\n",
       "      <td>41.60</td>\n",
       "      <td>10.40</td>\n",
       "      <td>58081.0</td>\n",
       "      <td>2000.3</td>\n",
       "      <td>4000.6</td>\n",
       "      <td>47.959</td>\n",
       "      <td>11592.1</td>\n",
       "      <td>9712.3</td>\n",
       "      <td>1855.7</td>\n",
       "      <td>771.2</td>\n",
       "      <td>192.8</td>\n",
       "      <td>68.89</td>\n",
       "      <td>137.78</td>\n",
       "      <td>1.6517</td>\n",
       "      <td>399.23</td>\n",
       "      <td>334.49</td>\n",
       "      <td>63.91</td>\n",
       "      <td>26.56</td>\n",
       "      <td>6.64</td>\n",
       "      <td>275.56</td>\n",
       "      <td>3.3034</td>\n",
       "      <td>798.46</td>\n",
       "      <td>668.98</td>\n",
       "      <td>127.82</td>\n",
       "      <td>53.12</td>\n",
       "      <td>13.28</td>\n",
       "      <td>0.039601</td>\n",
       "      <td>9.5719</td>\n",
       "      <td>8.0197</td>\n",
       "      <td>1.5323</td>\n",
       "      <td>0.6368</td>\n",
       "      <td>0.1592</td>\n",
       "      <td>2313.61</td>\n",
       "      <td>1938.43</td>\n",
       "      <td>370.37</td>\n",
       "      <td>153.92</td>\n",
       "      <td>38.48</td>\n",
       "      <td>1624.09</td>\n",
       "      <td>310.31</td>\n",
       "      <td>128.96</td>\n",
       "      <td>32.24</td>\n",
       "      <td>59.29</td>\n",
       "      <td>24.64</td>\n",
       "      <td>6.16</td>\n",
       "      <td>10.24</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>25/10/2017</td>\n",
       "      <td>14.99</td>\n",
       "      <td>10.59</td>\n",
       "      <td>74.08</td>\n",
       "      <td>20.22</td>\n",
       "      <td>70.98</td>\n",
       "      <td>44.02</td>\n",
       "      <td>26.96</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.06</td>\n",
       "      <td>6.89</td>\n",
       "      <td>1.43</td>\n",
       "      <td>5.04</td>\n",
       "      <td>4.23</td>\n",
       "      <td>61.46</td>\n",
       "      <td>368.85</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>9.36</td>\n",
       "      <td>4.29</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>93.9</td>\n",
       "      <td>31.9</td>\n",
       "      <td>340.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>252.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.260</td>\n",
       "      <td>41.7</td>\n",
       "      <td>46.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.9700</td>\n",
       "      <td>74.08</td>\n",
       "      <td>...</td>\n",
       "      <td>525.42</td>\n",
       "      <td>585.90</td>\n",
       "      <td>84.42</td>\n",
       "      <td>57.96</td>\n",
       "      <td>6.30</td>\n",
       "      <td>63504.0</td>\n",
       "      <td>2595.6</td>\n",
       "      <td>2721.6</td>\n",
       "      <td>65.520</td>\n",
       "      <td>10508.4</td>\n",
       "      <td>11718.0</td>\n",
       "      <td>1688.4</td>\n",
       "      <td>1159.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.09</td>\n",
       "      <td>111.24</td>\n",
       "      <td>2.6780</td>\n",
       "      <td>429.51</td>\n",
       "      <td>478.95</td>\n",
       "      <td>69.01</td>\n",
       "      <td>47.38</td>\n",
       "      <td>5.15</td>\n",
       "      <td>116.64</td>\n",
       "      <td>2.8080</td>\n",
       "      <td>450.36</td>\n",
       "      <td>502.20</td>\n",
       "      <td>72.36</td>\n",
       "      <td>49.68</td>\n",
       "      <td>5.40</td>\n",
       "      <td>0.067600</td>\n",
       "      <td>10.8420</td>\n",
       "      <td>12.0900</td>\n",
       "      <td>1.7420</td>\n",
       "      <td>1.1960</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>1738.89</td>\n",
       "      <td>1939.05</td>\n",
       "      <td>279.39</td>\n",
       "      <td>191.82</td>\n",
       "      <td>20.85</td>\n",
       "      <td>2162.25</td>\n",
       "      <td>311.55</td>\n",
       "      <td>213.90</td>\n",
       "      <td>23.25</td>\n",
       "      <td>44.89</td>\n",
       "      <td>30.82</td>\n",
       "      <td>3.35</td>\n",
       "      <td>21.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>26/10/2017</td>\n",
       "      <td>20.07</td>\n",
       "      <td>14.78</td>\n",
       "      <td>75.79</td>\n",
       "      <td>22.72</td>\n",
       "      <td>78.05</td>\n",
       "      <td>41.83</td>\n",
       "      <td>36.22</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.37</td>\n",
       "      <td>1.27</td>\n",
       "      <td>3.65</td>\n",
       "      <td>4.86</td>\n",
       "      <td>77.18</td>\n",
       "      <td>341.67</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.15</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.354</td>\n",
       "      <td>68.7</td>\n",
       "      <td>20.6</td>\n",
       "      <td>299.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>316.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>56.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>33.3400</td>\n",
       "      <td>75.79</td>\n",
       "      <td>...</td>\n",
       "      <td>939.56</td>\n",
       "      <td>549.46</td>\n",
       "      <td>151.06</td>\n",
       "      <td>9.96</td>\n",
       "      <td>9.96</td>\n",
       "      <td>99856.0</td>\n",
       "      <td>3507.6</td>\n",
       "      <td>4424.0</td>\n",
       "      <td>110.600</td>\n",
       "      <td>17885.6</td>\n",
       "      <td>10459.6</td>\n",
       "      <td>2875.6</td>\n",
       "      <td>189.6</td>\n",
       "      <td>189.6</td>\n",
       "      <td>123.21</td>\n",
       "      <td>155.40</td>\n",
       "      <td>3.8850</td>\n",
       "      <td>628.26</td>\n",
       "      <td>367.41</td>\n",
       "      <td>101.01</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.66</td>\n",
       "      <td>196.00</td>\n",
       "      <td>4.9000</td>\n",
       "      <td>792.40</td>\n",
       "      <td>463.40</td>\n",
       "      <td>127.40</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>19.8100</td>\n",
       "      <td>11.5850</td>\n",
       "      <td>3.1850</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>3203.56</td>\n",
       "      <td>1873.46</td>\n",
       "      <td>515.06</td>\n",
       "      <td>33.96</td>\n",
       "      <td>33.96</td>\n",
       "      <td>1095.61</td>\n",
       "      <td>301.21</td>\n",
       "      <td>19.86</td>\n",
       "      <td>19.86</td>\n",
       "      <td>82.81</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.46</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1528 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    性别  年龄        体检日期  *天门冬氨酸氨基转换酶  *丙氨酸氨基转换酶  *碱性磷酸酶  *r-谷氨酰基转换酶   *总蛋白  \\\n",
       "id                                                                          \n",
       "1    1  41  12/10/2017        24.96      23.10   99.59       20.23  76.88   \n",
       "2    1  41  19/10/2017        24.57      36.25   67.21       79.00  79.43   \n",
       "3    1  46  26/10/2017        20.82      15.23   63.69       38.17  86.23   \n",
       "4    0  22  25/10/2017        14.99      10.59   74.08       20.22  70.98   \n",
       "5    0  48  26/10/2017        20.07      14.78   75.79       22.72  78.05   \n",
       "\n",
       "      白蛋白   *球蛋白  白球比例  甘油三酯  总胆固醇  高密度脂蛋白胆固醇  低密度脂蛋白胆固醇    尿素     肌酐      尿酸  \\\n",
       "id                                                                              \n",
       "1   49.60  27.28  1.82  1.31  4.43       1.37       2.65  5.87  77.25  349.39   \n",
       "2   47.76  31.67  1.51  2.81  4.06       0.93       2.63  5.26  87.12  486.78   \n",
       "3   48.00  38.23  1.26  0.99  4.13       1.64       2.01  4.77  78.19  452.07   \n",
       "4   44.02  26.96  1.63  1.06  6.89       1.43       5.04  4.23  61.46  368.85   \n",
       "5   41.83  36.22  1.15  0.97  5.37       1.27       3.65  4.86  77.18  341.67   \n",
       "\n",
       "    乙肝表面抗原  乙肝表面抗体  乙肝e抗原  乙肝e抗体  乙肝核心抗体  白细胞计数  红细胞计数   血红蛋白  红细胞压积  红细胞平均体积  \\\n",
       "id                                                                              \n",
       "1     0.04    3.22   0.04   1.67    1.69   5.34   5.21  166.1  0.479     91.9   \n",
       "2     0.04    3.22   0.04   1.67    1.69   7.65   5.21  156.0  0.456     87.5   \n",
       "3     0.01    0.02   0.01   1.37    1.07   4.60   4.76  148.8  0.438     91.9   \n",
       "4     0.04    3.22   0.04   1.67    1.69   9.36   4.29  137.0  0.403     93.9   \n",
       "5     0.04    3.22   0.04   1.67    1.69   5.07   5.15  106.0  0.354     68.7   \n",
       "\n",
       "    红细胞平均血红蛋白量  红细胞平均血红蛋白浓度  红细胞体积分布宽度  血小板计数  血小板平均体积  血小板体积分布宽度  血小板比积  \\\n",
       "id                                                                         \n",
       "1         31.9        347.0       12.8  166.0      9.9       17.4  0.164   \n",
       "2         29.9        342.0       13.4  277.0      9.2       10.3  0.260   \n",
       "3         31.3        340.0       13.0  241.0      8.3       16.6  0.199   \n",
       "4         31.9        340.0       12.6  252.0     10.3       10.8  0.260   \n",
       "5         20.6        299.0       16.6  316.0     11.1       14.0  0.350   \n",
       "\n",
       "    中性粒细胞%  淋巴细胞%  单核细胞%  嗜酸细胞%  嗜碱细胞%  有无蛋白指标  有无固醇指标  有无血指标  有无细胞指标  有无酶指标  \\\n",
       "id                                                                             \n",
       "1     54.1   34.2    6.5    4.7    0.6       1       1      1       1      1   \n",
       "2     52.0   36.7    5.8    4.7    0.8       1       1      1       1      1   \n",
       "3     48.1   40.3    7.7    3.2    0.8       1       1      1       1      1   \n",
       "4     41.7   46.5    6.7    4.6    0.5       1       1      1       1      1   \n",
       "5     56.6   33.1    9.1    0.6    0.6       1       1      1       1      1   \n",
       "\n",
       "    有无尿指标  有无肝指标  缺失值数量   酶_mean  酶_max     ...       红细胞体积分布宽度*中性粒细胞%  \\\n",
       "id                                          ...                          \n",
       "1       1      0      5  41.9700  99.59     ...                 692.48   \n",
       "2       1      0      5  51.7575  79.00     ...                 696.80   \n",
       "3       1      1      0  34.4775  63.69     ...                 625.30   \n",
       "4       1      0      5  29.9700  74.08     ...                 525.42   \n",
       "5       0      0      8  33.3400  75.79     ...                 939.56   \n",
       "\n",
       "    红细胞体积分布宽度*淋巴细胞%  红细胞体积分布宽度*单核细胞%  红细胞体积分布宽度*嗜酸细胞%  红细胞体积分布宽度*嗜碱细胞%  \\\n",
       "id                                                                       \n",
       "1            437.76            83.20            60.16             7.68   \n",
       "2            491.78            77.72            62.98            10.72   \n",
       "3            523.90           100.10            41.60            10.40   \n",
       "4            585.90            84.42            57.96             6.30   \n",
       "5            549.46           151.06             9.96             9.96   \n",
       "\n",
       "    血小板计数*血小板计数  血小板计数*血小板平均体积  血小板计数*血小板体积分布宽度  血小板计数*血小板比积  血小板计数*中性粒细胞%  \\\n",
       "id                                                                           \n",
       "1       27556.0         1643.4           2888.4       27.224        8980.6   \n",
       "2       76729.0         2548.4           2853.1       72.020       14404.0   \n",
       "3       58081.0         2000.3           4000.6       47.959       11592.1   \n",
       "4       63504.0         2595.6           2721.6       65.520       10508.4   \n",
       "5       99856.0         3507.6           4424.0      110.600       17885.6   \n",
       "\n",
       "    血小板计数*淋巴细胞%  血小板计数*单核细胞%  血小板计数*嗜酸细胞%  血小板计数*嗜碱细胞%  血小板平均体积*血小板平均体积  \\\n",
       "id                                                                        \n",
       "1        5677.2       1079.0        780.2         99.6            98.01   \n",
       "2       10165.9       1606.6       1301.9        221.6            84.64   \n",
       "3        9712.3       1855.7        771.2        192.8            68.89   \n",
       "4       11718.0       1688.4       1159.2        126.0           106.09   \n",
       "5       10459.6       2875.6        189.6        189.6           123.21   \n",
       "\n",
       "    血小板平均体积*血小板体积分布宽度  血小板平均体积*血小板比积  血小板平均体积*中性粒细胞%  血小板平均体积*淋巴细胞%  \\\n",
       "id                                                                    \n",
       "1              172.26         1.6236          535.59         338.58   \n",
       "2               94.76         2.3920          478.40         337.64   \n",
       "3              137.78         1.6517          399.23         334.49   \n",
       "4              111.24         2.6780          429.51         478.95   \n",
       "5              155.40         3.8850          628.26         367.41   \n",
       "\n",
       "    血小板平均体积*单核细胞%  血小板平均体积*嗜酸细胞%  血小板平均体积*嗜碱细胞%  血小板体积分布宽度*血小板体积分布宽度  \\\n",
       "id                                                                     \n",
       "1           64.35          46.53           5.94               302.76   \n",
       "2           53.36          43.24           7.36               106.09   \n",
       "3           63.91          26.56           6.64               275.56   \n",
       "4           69.01          47.38           5.15               116.64   \n",
       "5          101.01           6.66           6.66               196.00   \n",
       "\n",
       "    血小板体积分布宽度*血小板比积  血小板体积分布宽度*中性粒细胞%  血小板体积分布宽度*淋巴细胞%  血小板体积分布宽度*单核细胞%  \\\n",
       "id                                                                        \n",
       "1            2.8536            941.34           595.08           113.10   \n",
       "2            2.6780            535.60           378.01            59.74   \n",
       "3            3.3034            798.46           668.98           127.82   \n",
       "4            2.8080            450.36           502.20            72.36   \n",
       "5            4.9000            792.40           463.40           127.40   \n",
       "\n",
       "    血小板体积分布宽度*嗜酸细胞%  血小板体积分布宽度*嗜碱细胞%  血小板比积*血小板比积  血小板比积*中性粒细胞%  血小板比积*淋巴细胞%  \\\n",
       "id                                                                             \n",
       "1             81.78            10.44     0.026896        8.8724       5.6088   \n",
       "2             48.41             8.24     0.067600       13.5200       9.5420   \n",
       "3             53.12            13.28     0.039601        9.5719       8.0197   \n",
       "4             49.68             5.40     0.067600       10.8420      12.0900   \n",
       "5              8.40             8.40     0.122500       19.8100      11.5850   \n",
       "\n",
       "    血小板比积*单核细胞%  血小板比积*嗜酸细胞%  血小板比积*嗜碱细胞%  中性粒细胞%*中性粒细胞%  中性粒细胞%*淋巴细胞%  \\\n",
       "id                                                                       \n",
       "1        1.0660       0.7708       0.0984        2926.81       1850.22   \n",
       "2        1.5080       1.2220       0.2080        2704.00       1908.40   \n",
       "3        1.5323       0.6368       0.1592        2313.61       1938.43   \n",
       "4        1.7420       1.1960       0.1300        1738.89       1939.05   \n",
       "5        3.1850       0.2100       0.2100        3203.56       1873.46   \n",
       "\n",
       "    中性粒细胞%*单核细胞%  中性粒细胞%*嗜酸细胞%  中性粒细胞%*嗜碱细胞%  淋巴细胞%*淋巴细胞%  淋巴细胞%*单核细胞%  \\\n",
       "id                                                                       \n",
       "1         351.65        254.27         32.46      1169.64       222.30   \n",
       "2         301.60        244.40         41.60      1346.89       212.86   \n",
       "3         370.37        153.92         38.48      1624.09       310.31   \n",
       "4         279.39        191.82         20.85      2162.25       311.55   \n",
       "5         515.06         33.96         33.96      1095.61       301.21   \n",
       "\n",
       "    淋巴细胞%*嗜酸细胞%  淋巴细胞%*嗜碱细胞%  单核细胞%*单核细胞%  单核细胞%*嗜酸细胞%  单核细胞%*嗜碱细胞%  \\\n",
       "id                                                                    \n",
       "1        160.74        20.52        42.25        30.55         3.90   \n",
       "2        172.49        29.36        33.64        27.26         4.64   \n",
       "3        128.96        32.24        59.29        24.64         6.16   \n",
       "4        213.90        23.25        44.89        30.82         3.35   \n",
       "5         19.86        19.86        82.81         5.46         5.46   \n",
       "\n",
       "    嗜酸细胞%*嗜酸细胞%  嗜酸细胞%*嗜碱细胞%  嗜碱细胞%*嗜碱细胞%  \n",
       "id                                         \n",
       "1         22.09         2.82         0.36  \n",
       "2         22.09         3.76         0.64  \n",
       "3         10.24         2.56         0.64  \n",
       "4         21.16         2.30         0.25  \n",
       "5          0.36         0.36         0.36  \n",
       "\n",
       "[5 rows x 1528 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:5.34337\n",
      "Will train until train-rmse hasn't improved in 50 rounds.\n",
      "[1]\ttrain-rmse:5.31513\n",
      "[2]\ttrain-rmse:5.28707\n",
      "[3]\ttrain-rmse:5.25905\n",
      "[4]\ttrain-rmse:5.23128\n",
      "[5]\ttrain-rmse:5.20365\n",
      "[6]\ttrain-rmse:5.17628\n",
      "[7]\ttrain-rmse:5.14907\n",
      "[8]\ttrain-rmse:5.12193\n",
      "[9]\ttrain-rmse:5.09509\n",
      "[10]\ttrain-rmse:5.06834\n",
      "[11]\ttrain-rmse:5.04175\n",
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      "[13]\ttrain-rmse:4.98911\n",
      "[14]\ttrain-rmse:4.9632\n",
      "[15]\ttrain-rmse:4.93729\n",
      "[16]\ttrain-rmse:4.91144\n",
      "[17]\ttrain-rmse:4.88594\n",
      "[18]\ttrain-rmse:4.86055\n",
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    {
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     "text": [
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    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2892]\ttrain-rmse:1.17653\n",
      "[2893]\ttrain-rmse:1.17642\n",
      "[2894]\ttrain-rmse:1.17635\n",
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      "[2998]\ttrain-rmse:1.1703\n",
      "[2999]\ttrain-rmse:1.17023\n"
     ]
    }
   ],
   "source": [
    "from xgboost.sklearn import XGBRegressor\n",
    "# from sklearn.metrics import f1_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import ElasticNet,LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "#  数据集\n",
    "# most_important = feature_importance['feature'].loc[:50]\n",
    "# poly = PolynomialFeatures(2)\n",
    "# train_data_most_important = pd.DataFrame(poly.fit_transform(train_data[most_important]),index=train_data.index)\n",
    "# train_data = pd.concat([train_data.drop(most_important,axis=1),train_data_most_important],axis=1)\n",
    "# test_data_most_important = pd.DataFrame(poly.transform(test_data[most_important]),index=test_data.index)\n",
    "# test_data = pd.concat([test_data.drop(most_important,axis=1),test_data_most_important],axis=1)\n",
    "#training xgboost\n",
    "train_data.drop(['体检日期'],axis=1,inplace=True)\n",
    "test_data.drop(['体检日期'],axis=1,inplace=True)\n",
    "dtrain = xgb.DMatrix(train_data,label=targets)\n",
    "dtest = xgb.DMatrix(test_data)\n",
    "# 交叉验证\n",
    "params={'booster':'gbtree',\n",
    "    'objective': 'reg:linear',\n",
    "    'eval_metric': 'rmse',\n",
    "    'max_depth':3,\n",
    "    'lambda':200,\n",
    "    'subsample':0.75,\n",
    "    'colsample_bytree':0.7,\n",
    "    'eta': 0.006,#0.002\n",
    "    'seed':1024,\n",
    "    'nthread':4,\n",
    "    'reg_alpha':0.1\n",
    "    }\n",
    "\n",
    "# 设置参数\n",
    "watchlist  = [(dtrain,'train')]\n",
    "\n",
    "model = xgb.train(params,dtrain,num_boost_round=3000,evals=watchlist,early_stopping_rounds=50)\n",
    "\n",
    "pre_y = model.predict(dtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.68255054944\n"
     ]
    }
   ],
   "source": [
    "print(mse(test_real_y,pre_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.67513493916\n"
     ]
    }
   ],
   "source": [
    "pre_y = model.predict(dtest)\n",
    "print(mse(test_real_y,pre_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.65532476475\n"
     ]
    }
   ],
   "source": [
    "print(mse(test_real_y,pre_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6536369937\n"
     ]
    }
   ],
   "source": [
    "print(mse(test_real_y,pre_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.65513232487\n"
     ]
    }
   ],
   "source": [
    "print(mse(test_real_y,pre_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(pre_y)\n",
    "results.to_csv('../results.csv',index=False,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet,LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# train_x,test_x,train_y,test_y = train_test_split(train_data,targets,test_size=0.5)\n",
    "enet = ElasticNet(alpha=1, l1_ratio=0,normalize=True,tol=0.01)\n",
    "# train.drop(['体检日期'],axis=1,inplace=True)\n",
    "# test.drop(['体检日期'],axis=1,inplace=True)\n",
    "pre_y = enet.fit(train, targets).predict(test)\n",
    "# 预测\n",
    "pre_y[pre_y >20] = 20\n",
    "pre_y[pre_y<3] = 3\n",
    "print(mse(test_real_y,pre_y))\n",
    "pre_train_y = enet.predict(train)\n",
    "print(mse(pre_train_y,targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6642, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.apply(lambda x : np.sort(x)[-2],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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